In [1]}, the parameters of \(SARSA(\lambda )\)  and \(Q(\lambda )\)  as two RL algorithms based on eligibility traces are optimized using Genetic Algorithm (GA). In this method, a vector containing all hyper-parameters is considered as a chromosome and the mutation and cross-over are performed on this vector. The algorithm is tested on under-actuated pendulum swing-up and the authors show that the selected parameters maximize the end performance. Another application of GA for optimizing the hyper-parameters of RL algorithms is presented in [2]}, where the parameters of Deep Deterministic Policy Gradient (DDPG) with Hindsight Experience Replay (HER) [3]} are learned through GA. The target parameters for GA are discount factor, polyak-averaging coefficient which is used for updating target networks in algorithms like DQN with different target and main networks, learning rate of actor and critic networks, percentage of time a random action is taken and Gaussian noise parameters. Concatenation of binary representation of these parameters builds the chromosomes and the fitness is the inverse of number of epochs to reach close to maximum success rate for a particular task.
